The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.

Prostate cancer (PCa) diagnosis has historically relied on the prostate-specific antigen (PSA) testing. Although the screening significantly reduces mortality rates, PSA has low specificity with risks of overdiagnosis and overtreatment. These limitations highlight the need for a more accurate diagnostic approach. Emerging technologies, such as artificial intelligence (AI), novel biomarkers, and advanced imaging techniques, offer promising avenues to enhance the accuracy and efficiency of PCa diagnosis and risk stratification. This narrative review comprehensively analyzed the current literature, focusing on new tools aiding PCa diagnosis (AI-driven image interpretation, radiomics, genomic classifiers, biomarkers, and multimodal data integration) with consideration for technical, regulatory, and ethical challenges related to clinical implementation of AI-based technologies. A literature search was performed using the PubMed and MEDLINE databases to identify relevant peer-reviewed articles published in English using the search terms “prostate cancer,” “artificial intelligence,” “machine learning,” “deep learning,” “MRI,” “histopathology,” and “diagnosis.” Articles were selected based on their relevance to AI-assisted diagnostic tools, clinical utility, and performance metrics. In addition, a separate section was developed initially to contextualize the limitations of current PSA-based screening approaches. The reviewed studies showed that AI had significant utility in prostate mpMRI interpretation (lesion detection; Gleason grading) with high accuracy and high reproducibility. For the pathologist, AI-driven algorithms improve the diagnostic accuracy of digital slide evaluation for histologic diagnosis of prostate cancer and automated Gleason score grading. Genomic tools such as the Oncotype DX test, combined with AI, could also allow for tailored and individualized risk prediction. Overall, multimodal models integrating clinical, imaging, and molecular data often outperform traditional PSA-based strategies and reduce unnecessary biopsies. Transition from PSA-centered toward AI-driven, biomarker-supported, and image-enhanced diagnosis marks a critical evolution in PCa diagnosis.